The present invention relates to automated detection and classification of prostate tumors in medical images, and more particularly, to automated detection and classification of prostate tumors in multi-parametric magnetic resonance (MR) images using a deep learning network.
Prostate cancer is the most frequently diagnosed cancer in American men with 181,000 new cases in 2016 resulting in more than 26,000 deaths. Early diagnosis often results in long term survival, but depends on invasive multicore biopsies that are typically guided by transrectal ultrasound (TRUS) imaging. Recently, multi-parametric magnetic resonance imaging (MRI) has shown promising results for use in non-invasive prostate cancer detection, as a strong correlation has been established between mpMRI and histological data. Prostate biopsies, whether blind or guided, are invasive and painful to the patient, whereas acquiring a multi-parametric MRI image set is substantially less invasive. If the same level of clinical decisions can be made using multi-parametric MRI, some or all of such invasive biopsies can be avoided and replaced with non-invasive “virtual biopsies” using multi-parametric MRI.
Two specific tasks are required in examination of multi-parametric magnetic resonance (MR) images. First, potential cancer regions must be detected, and second, these suspicious areas must be classified as benign or otherwise actionable (recommend biopsy). Manual reading multi-parametric MR images, which consist of as many as eight image channels, can be a tedious task. Furthermore, subtle and collective signatures of a cancerous lesion expressed within multi-parametric MR images are difficult to detect, even by an expert radiologist. Accordingly, a method for automated detection and classification of prostate tumors/lesions in multi-parametric MR images is desirable.